Normative References for Graphomotor and Latency Digital Clock Drawing Metrics for Adults Age 55 and Older: Operationalizing the Production of a Normal Appearing Clock
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nihms-1721672.pdf
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Accepted version
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610.16 KB
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Author(s)
Davoudi, Anis
Dion, Catherine
Formanski, Erin
Frank, Brandon E
Amini, Shawna
Matusz, Emily F
Wasserman, Victor
Penney, Dana
Davis, Randall
Rashidi, Parisa
Date Issued
2021
Journal
Journal of Alzheimer's Disease
Publisher
IOS Press
Citation
Davoudi, Anis, Dion, Catherine, Formanski, Erin, Frank, Brandon E, Amini, Shawna et al. 2021. "Normative References for Graphomotor and Latency Digital Clock Drawing Metrics for Adults Age 55 and Older: Operationalizing the Production of a Normal Appearing Clock." Journal of Alzheimer's Disease, 82 (1).
Version
Author's final manuscript
Abstract
Background: Relative to the abundance of publications on dementia and clock drawing, there is limited literature operationalizing ‘normal’ clock production. Objective: To operationalize subtle behavioral patterns seen in normal digital clock drawing to command and copy conditions. Methods: From two research cohorts of cognitively-well participants age 55 plus who completed digital clock drawing to command and copy conditions (n = 430), we examined variables operationalizing clock face construction, digit placement, clock hand construction, and a variety of time-based, latency measures. Data are stratified by age, education, handedness, and number anchoring. Results: Normative data are provided in supplementary tables. Typical errors reported in clock research with dementia were largely absent. Adults age 55 plus produce symmetric clock faces with one stroke, with minimal overshoot and digit misplacement, and hands with expected hour hand to minute hand ratio. Data suggest digitally acquired graphomotor and latency differences based on handedness, age, education, and anchoring. Conclusion: Data provide useful benchmarks from which to assess digital clock drawing performance in Alzheimer’s disease and related dementias.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.3233/JAD-201249
